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Full text of "A Sensitive Wavelet-Based Algorithm for Fault Detection in Power Distribution Networks"

ACEEE Int. J. on Communication, Vol. 02, No. 01, Mar 2011 



A Sensitive Wavelet-Based Algorithm for Fault 
Detection in Power Distribution Networks 



N. Zamanan M. Gilany 

College of Technological Studies, 

Dept. of Electrical Engineering, 

Kuwait 

W. Wahba 

Faculty of Engineering, 

Faoum University, 

Egypt 



Abstract — This paper presents a wavelet based technique for 
detection and classification of abnormal conditions that occur 
on power distribution lines. The transients associated with these 
conditions contain a large spectrum of frequencies, which are 
analyzed using wavelet transform approach. The proposed 
technique depends on a sensitive fault detection parameter 
(denoted SFD) calculated from wavelet multi-resolution 
decomposition of the three phase currents. The simulation 
results of this study clearly indicate that the proposed technique 
can be successfully used to detect not only faults that could not 
be detected by conventional relays but also abnormal transients 
like load switching and inrush currents. 

Keywords — Wavelet Transform, Fault detection, Distribution 
Networks, Inrush currents. 

I.Introduction 

Power disturbance occur due to changes in the 
electrical configurations of a power circuit. Disturbance 
causing failure are infrequent compared to the number of 
disturbances that occur every day due to normal system 
operations (switching of lines, switching on/off generating 
units, or switching of capacitor banks to balance inductive 
loads). In order to improve electrical power quality, one must 
have the ability to detect and classify these disturbances. 
There are two main problems related to protection of 
distribution networks. 

1. The first problem is the faults through high resistance. 
These faults are not easy to be detected, since the fault 
current may not reach the setting of the relay. It may 
cause successive heating and fires unless the fault is 
isolated. 

2. The second problem is the numerous cases of power 
system transients (like switching) which may cause 
transient responses similar to that induced by the 
permanent faults. It is hence necessary to identify the 
disturbance type and classify it in order to have a high 
reliability levels. 

The waveforms associated with these transients are 
typically non-periodic signals, which contain both high 
frequency oscillations and localized impulses superimposed 
on the power frequencies. In order to extract or separate these 

©2011 ACEEE 

DOI: 01.IJCom.02.01.180 



superimposed signals, several algorithms have been 
introduced in power system such as Kalman filtering, least 
square method, and Fourier transform. However, in presence 
of non-stationary signals, the performance of these 
algorithms is limited and this is the introduction to the need 
of wavelet transform (WT). 

The idea of application of wavelet transform analysis to 
fault detection in power systems is not new and there are 
hundreds of publications related to this idea. The wavelet- 
based techniques are applied in different power system 
applications such as detecting arcing faults in distribution 
systems [1], locating SLG faults in distribution lines [2], 
stator ground fault protection schemes with selectivity for 
generators [3], locating faults in transmission systems [4,5], 
locating faults in systems with tapped lines [6] and solving 
inrush current problems [7,8]. 

Application of wavelet transform in protection of 
distribution networks faces two main difficulties that have 
limited the usefulness of these techniques. The first difficulty 
is that the transient levels in distribution circuits are generally 
small compared with that in HV transmission networks. The 
second difficulty faces the application of wavelet transforms 
in protection of distribution networks is the need to add extra 
components (e.g. VTs) to the existing distribution protection 
systems in order to apply such techniques. Analyzing voltage 
waveforms is much easier than current waveforms due to 
the large content of harmonics in voltage waveforms. Most 
researchers are using both current and voltage samples of 
the three phases as inputs for fault detection in order to 
overcome this difficulty [1,2,3,6,7,8]. However, many 
distribution systems are not provided with voltage 
transformers since they are using overcurrent relays. Other 
researchers use extra hardware like GPS to improve the 
usefulness of these techniques [6, 9]. 

A sensitive parameter (denoted SFD) is used in this paper 
to detect and classify faults in distribution networks under 
different operating conditions including low-current faults 
and arcing faults. The technique can also be used to 
discriminate between the transients due to switching-on 
additional loads and that due to 3LG faults through high 
resistance. In all cases, the proposed technique is not affected 



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ACEEE Int. J. on Communication, Vol. 02, No. 01, Mar 2011 



with the above mention two limitations (low harmonic 
contents or need for voltage signals). It also discriminates 
between the inrush currents and fault currents. In the next 
section, a theoretical background of wavelet theory is 
reviewed. The proposed algorithm and the simulation results 
are then presented in the subsequent sections. 

II.Theoretical Background 

In order to extract certain information from a given signal, 
mathematical transformations are required. WT is very well 
suited for wideband signals that are not periodic and may 
contain both sinusoidal and impulse transients, as is typical 
in power system transients. In particular, the ability of 
wavelets to focus on short-time intervals for high frequency 
components and long-time intervals for low frequency 
components improves the analysis of signals with localized 
impulses and oscillations, particularly in the presence of 
fundamental and low-order harmonics [9]. 

The Wavelet Transform (WT) of a continuous time domain 
signal f(t) is defined as: 






dt (1) 



Where a is the scale constant (dilation) and b is the 
translation constant (time shift). The 0(t) is the wavelet 
function that is short, oscillatory with zero average and 
decays quickly at both ends. This property of 0(t) ensures 
that the integral in equation (1) is finite and that is why the 
name wavelet or "small wave" is assigned to the transform. 
The term 0(t) is referred to as the "mother wavelet" and its 
dilates (a) and translates (b) simply are referred to as " 
wavelets". 

Wavelets have a window that is automatically adapted to 
give an appropriate resolution. The window is shifted along 
the signal and for every position the spectrum is calculated. 
This process is repeated many times with a slightly shorter 
(or longer) window for every new cycle. At the end of this 
process, the result will be a collection of time-frequency 
representations of the signal, all with different resolutions. 
Because of this collection of representations we can speak 
of a multi-resolution analysis (MRA) [10]. 

The wavelet transform given by Eq. (1) has a digital 
counterpart known as the Discrete Wavelet Transform 
(DWT). The DWT is defined as 



Dm(f,m,n)=-f=Yf(kyr 

V^O k 



n-ka^ 



(2) 



where the parameters a and b in Eq. (1) are replaced by a™ 

and (ka™ ). The parameters k and m are integer variables. 

The actual implementation of the DWT involves 
successive pairs of high-pass and low-pass filters at each 



scaling stage of the wavelet. The successive stages of 
decomposition are known as levels or details (denoted 
detail_l or dl for short, detail_2 or d2 for short, etc.). The 
multi-resolution analysis, MRA details at various levels 
contain the features that can be used for detection and 
classification of faults. More details about wavelet transform 
can be found in [11-12]. 

The choice of mother wavelet, 0(t) plays a significant 
role in detecting and localizing different types of fault 
transients. Each mother function has its own features 
depending on the application requirements. The proposed 
technique is depending on detecting and analyzing low 
amplitude, short duration, fast decaying high frequency 
current signals. In this study, Daubechies wavelets (D4) is 
chosen since it is effective for the detecting fast and short 
transient disturbances [13]. 

III.The Proposed Algorithm 

In the proposed technique, the wavelet transform is firstly 
applied to decompose the three phase current signals into a 
series of detailed wavelet components, each of which is a 
time domain signal that covers a specific frequency band, 
hence the time and frequency domains features of the 
transient signals are extracted. 

Wavelet analysis involves selection of an appropriate 
wavelet function called "mother wavelet". The choice of 
mother wavelet plays a significant role in detecting and 
localizing different types of fault transients. Each mother 
function has its feasibility depending on the application 
requirements. In this study we are interested in detecting 
and analyzing low amplitude, short duration, fast decaying 
and oscillating type of high frequency current signals. One 
of the most popular mother wavelets suitable for such 
applications is the daubichies's wavelet. In this paper, D4 
wavelet is used for the analysis of the current waveforms. 

For each cycle, the detail signals dl and d2 of each of the 
three-phase currents are calculated. The sensitive fault 
detection parameter used in this work, SDF is a moving 
average filter for the summation of the squared of dl and d2 
of the three phase currents. Involving only two levels details 
ensures less computational burden and fast speed. This 
parameter is calculated as follows: 

SFD p (k) = SFD p (k -1)+ i///(A-)-i d p 2 (k-n), 



!j=1 



f!=l 



pe(a.b.c) 



(3) 



where n is the number of samples in the window, h 
is the suffix for the detail order (1 or 2 ) and p = (a, b, c) are 
suffixes used for phases. 

A fault is detected if the value of SFD exceeds a 
threshold setting (equal to 300 in this study). This threshold 
is selected according to the detail values in normal and fault 
operations. The previously-mentioned limitations for 
applying wavelet in distribution networks are reduced in the 



© 2011 ACEEE 

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ACEEE Int. J. on Communication, Vol. 02, No. 01, Mar 2011 



proposed technique for the two following reasons: 

The proposed technique concentrates on the noise 
frequency of the signal not on the noise amplitude. The 
proposed technique uses the parameter SFD over one cycle 
of the current signal which is very sensitive to any small 
changes in the current signal since it uses the squared of the 
first and second details of the decomposed signals. 

IV.MODELING OF POWER SYSTEM 

The power system under study was modeled using the 
Matlab power system toolbox. Simulations for fault analysis 
are carried out on the low voltage side of a 66/11 KV 
transformer connected to two 20 km-feeders. The 20 km 
overhead feeder is modeled using the distributed parameters. 
A sampling rate of 20 kHz is used, which covers the range 
of frequencies from 10 kHz to DC. To prove the sensitivity 
of the proposed technique, all the simulation studies are done 
with fault current magnitudes lower than the typical 
overcurrent relay settings. Some fault cases are carried out 
with a fault current lower than the normal load current. Only 
the three phase current signals are used in order to avoid 
adding extra components (voltage transformers) to the typical 
existing protection system in distribution networks. 

V. Simulation Results 

Intensive simulations for a large number of case 
studies were carried out, taking into consideration different 
normal and abnormal conditions. The simulation study using 
Matlab is adopted to generate the current signals of the power 
system under study. The current signals are then decomposed 
into different levels of frequencies. The SFD (given in A 2 ) is 
calculated using only detail_l and detail_2 for each phase. 
The predetermined threshold is compared to the calculated 
SFD and accordingly, the fault is detected and classified. 
The performance of the proposed technique under different 
conditions is illustrated in the following sub- sections. 

A. High resistive SLG faults 

In this case, a SLG fault is simulated at a distance 
of 10 km. The fault resistance is chosen, such that the fault 
current does not exceed 200% of the rated load current. This 
is the typical pickup setting for ordinary overcurrent relays. 
The details (dl and d2) of the faulty phase current is shown 
in Fig. 1, where that of a healthy phase (phase-B as an 
example) are shown in Fig. 1. 







1:1 i I: ""1 3*3 
5a ~q « I 



Fig. 1: Detail_l and detail_2 for the faulty phase (SLG fault) 



The waveforms of the healthy phases may contain 
high frequency signals due to mutual coupling as shown in 
Fig. 2. The SFD of the healthy phase is very small compared 
to that of the faulty phase as shown in Fig. 3. 



•0.1 



147219 Ofc US *i 



I— 

i US 391 



. : : 
d c 

-: I 



¥ 



US i-91 4^1 £41 r& 



Fig. 2: Detail_l and detail_2 for a healthy phase (SLG fault). 



— : '.'■ 
7 



II 



«,C6 ■ 

- : - . 



n 



1 ISC ;*3xa* 5&* 



Fig. 3 : The SFD for the faulty and a healthy phase for a SLG 
fault. 



B. 



High resistive 3 LG fault 

In this case, a (3LG) fault is considered. To add 
more difficulties to this fault case, it is assumed that the fault 
occurred near to the far end of the feeder (at a distance of 1 8 
km from the sending end). The inception angle is 120°. The 
three phase currents are shown in Fig. 4. The corresponding 
wavelet details for one of the three faulty phases are shown 
in Fig. 5. 



2000 -, 




1000 - 




urrent 




o 

-1000 - 


^(w^eA^Sj X 33 X 91 }( 


-2000 - 


Samples 



Fig. 4: The three phase currents for 3LG fault. 



i - 






63- 








■B,- _ 




h 




ik m b 


■ \ 


i r; 


*] 


=>W*2 





: ., 
I 



-« J 



2*1 C5 1*7 



■^r 



2-nzt « 



Fig. 5: The decomposition for one of the three phase currents (for 
3LG fault). 

The SFD for this case is shown later in Fig 7(a2, 
b2, c2) in order to be compared with the case presented in 
the next subsection (switching on additional load). 

C. Load switching 

Transients may be initiated due to switching opera- 
tions e.g. when adding a new load to the system. Discrimi- 
nation between the transients generated due to switching con- 
dition and that due to faults is of great importance. Figure 6 
shows the waveforms of the three phase currents during 
switching condition. The inception angle was chosen to be 
120° to create some sort of analogy between this switching 
condition and the case presented in the previous section (3LG 
fault). 



©2011 ACEEE 

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ACEEE Int. J. on Communication, Vol. 02, No. 01, Mar 2011 




°-1000.0 -! 



-2000.0 -J 



Samples 



Fig. 6: Three phase currents for load switching 

The similarity between the two cases is more critical if 
the low frequency signals are considered, and hence, a false 
trip may not be avoided. The proposed technique avoids this 
problem since the proposed technique uses only the high 
frequency signals (details 1-2 only). 

The SFD of these frequency bands (shown in Fig. 7(al, 
bl, cl)) are less than the predetermined threshold, therefore 
such cases will be recognized as switching conditions and 
not fault conditions since all of the SFDs exceeds the 
threshold (Threshold = 300). 




a MM . 
SX , 

■ 


n 




IJ1 4£1 6*1 K1 



al. SFD (load switching) 



a2. SFD (3LG fault) 




ft* 6 - 

■ 


n 




Z5* «T 5T S9& 



bl. SFD (load s v. itching) 



J3Z SFD (3LG fault) 




iCOC , 

<B 1«B . 
. 


n 




1 33- «5 SBT" 9C9 



D. 



Fig. 7: A Comparison between the SFD for 3LG fault and the SFD 
for load switching 

Discrimination between Permanent Faults and 
Inrush currents 



When a transformer is switched off, its core gener- 
ally retains some residual flux. Later, when the transformer 
is re-energized, the core is likely to saturate. If it does, the 
primary windings draw large magnetizing currents from the 
power system. This phenomenon is known as magnetizing 
inrush and is characterized by the transformer drawing large 
currents from the source but supplying relatively smaller 
currents to the loads. This results in a large differential cur- 
rent which causes differential relay to operate. But it is not a 
fault condition, and therefore the relay must be able to dis- 
criminate inrush current from internal fault current, and re- 
main stable during inrush current. 



To date, there are many discrimination methods 
[7,8,14,15]. Each of these methods has its own shortcoming. 
For the second harmonic restrained method, under some 
special conditions, such as when the power transformer is 
connected to a long transmission line, or when the current 
transformer (CT) is saturated, the second harmonic 
component of the transformer current will increase, thereby 
affecting the operation of the relay. 

In this paper, the sensitive SFD parameter is examined 
against this problem. The results show that tracing the value 
of this parameter gives an excellent index for the 
discrimination between the permanent faults and inruch 
currents as shown in the following results. 

The inrush condition is simulated at different inception 
angles to almost cover all possible inrush current waveforms. 
Figure 8 shows the waveforms of one of the phase currents 
( IA). The inception angle is assumed to be zero. The 
decomposed signals (details) of the discrete wavelet 
transform are shown in Fig. 9. 




222 443 £64 885 1 106 1327 1548 1769 1930 



Samples 
Fig. S^ A typical inrush current case 



^s can De seen rrom tt 

■..-.■.■.■ v ■..■■..■■.■.■■■. A .-■■■.. ■■.■■■ ■■■■ ■..■.■■..■.■■■. .-. .-. -..-. ■ ' 

distorted quite significantly. 




SntWI 15012001 



9. Detaill and detail_2 for inrush current in phase-A with zero inception 

angle. 

The existence of the higher frequency signals 
superimposed on the fundamental can clearly demonstrated 
by wavelet transform. In this study, details 1-2 are only the 
ones employed in the analysis and feature extraction. For 
inrush condition, the high frequencies are of small magnitude 
compared with low frequencies. This feature is so important 
in the discrimination between internal faults and inrush 
currents. The value of SFD for all the examined cases are 
will below the setting threshold value as shown in Fig. 10. 



© 2011 ACEEE 

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ACEEE Int. J. on Communication, Vol. 02, No. 01, Mar 2011 



300 n 




200 ■ 




Q 

«100 - 
- 


n 




501 1001 1501 
Samples 



Ei&JJk JMJSE&l for phase-A {inrush current case) 
VI.CONCLUSIONS 

Distribution feeders are subjected to all types of 
faults. Detection of these faults and discriminating it from 
other transient conditions such as switching is of great 
importance. These faults can occur through resistances, that 
make the fault current not in the range of the setting of 
conventional relays and hence the fault will be not easy to 
be detected. The use of wavelet transform with the proposed 
sensitive SFD parameter makes it easy not only to detect the 
occurrence of a fault and its type but also to discriminate 
between transients due to switching conditions/inrush 
currents and that due to faults. A subroutine is designed to 
detect arcing faults in which the level of fault current is very 
small. Lots of simulation results show that the proposed 
method can exactly and effectively detect and classify both 
normal and abnormal conditions in the distribution networks. 

Acknowledgments 

The financial support from the PAAET in Kuwait (Project 
No. TS-09-02 ) is highly appreciated. 

References 

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